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How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest...

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Epoch AI

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178,703 views • 6 months ago •via X (Twitter)

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Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?
17:01

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Epoch AI

12,920 views • 9 months ago

Exclusive: Google just released two upgraded Gemini 1.5 models, 1.5-Pro-002 and 1.5-Flash-002 — achieving new, state-of-the-art performance across math. I sat down with Logan Kilpatrick (Logan Kilpatrick) to discuss the models, AI agents, AGI, and more. Timestamps: 00:00 Intro 01:01 Google rolls out two new Gemini models 2:18 What makes the new models so unique 3:40 Math improvements 8:20 Examples of Gemini 1.5 in the real world 10:19 Future problems AI could solve 12:54 Advantages of Gemini 1.5 16:50 Where beginners can get started 19:01 Advice to thrive in the new age of AI 22:02 Turning notes into podcasts 26:30 AI agents: Definitions and what they can do 31:22 What’s the final form for agents? 33:26 Proactive AI agent systems 36:00 Context windows in the agent era 41:01 AGI’s definition in Logan’s eyes 42:05 The current bottlenecks towards AGI
46:25

Exclusive: Google just released two upgraded Gemini 1.5 models, 1.5-Pro-002 and 1.5-Flash-002 — achieving new, state-of-the-art performance across math. I sat down with Logan Kilpatrick (Logan Kilpatrick) to discuss the models, AI agents, AGI, and more. Timestamps: 00:00 Intro 01:01 Google rolls out two new Gemini models 2:18 What makes the new models so unique 3:40 Math improvements 8:20 Examples of Gemini 1.5 in the real world 10:19 Future problems AI could solve 12:54 Advantages of Gemini 1.5 16:50 Where beginners can get started 19:01 Advice to thrive in the new age of AI 22:02 Turning notes into podcasts 26:30 AI agents: Definitions and what they can do 31:22 What’s the final form for agents? 33:26 Proactive AI agent systems 36:00 Context windows in the agent era 41:01 AGI’s definition in Logan’s eyes 42:05 The current bottlenecks towards AGI

Rowan Cheung

269,830 views • 1 year ago

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models
1:38:17

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models

Ashlee Vance

80,718 views • 7 months ago

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 views • 3 months ago

🚨Leaked🚨 Dave M. Portnoy vs Nick O’Neill #jailstool feud! 💥 🥊 Channel 9 News Super Bowl drama! 00:00 - The #jailstool token goes crazy 00:11 - Why you are poor 00:19 - Jailstool token takes over twitter 00:26 - What whales aped in 00:35 - Feud! Between Portnoy and Nick O Neil 00:39 - Dave calls nick a DOUCHE 00:46 - The HARD evidence 01:01 - Nick: WOW shots fired! 01:10 - How Portnoy DESTROYED all of web3 marketing 01:24 - Super Bowl will Mahomes and Taylor Swift CRY!? 01:30 - $Ket coin is RIPPING! 01:41 - Avalanche🔺 and AI 01:42 - What will the Wolves Dao AI be? 01:44 - AI agents platforms to look for: WWA 01:54 - Can AI beat a glass of Scotch? 02:05 - How to be Handsome!
2:11

🚨Leaked🚨 Dave M. Portnoy vs Nick O’Neill #jailstool feud! 💥 🥊 Channel 9 News Super Bowl drama! 00:00 - The #jailstool token goes crazy 00:11 - Why you are poor 00:19 - Jailstool token takes over twitter 00:26 - What whales aped in 00:35 - Feud! Between Portnoy and Nick O Neil 00:39 - Dave calls nick a DOUCHE 00:46 - The HARD evidence 01:01 - Nick: WOW shots fired! 01:10 - How Portnoy DESTROYED all of web3 marketing 01:24 - Super Bowl will Mahomes and Taylor Swift CRY!? 01:30 - $Ket coin is RIPPING! 01:41 - Avalanche🔺 and AI 01:42 - What will the Wolves Dao AI be? 01:44 - AI agents platforms to look for: WWA 01:54 - Can AI beat a glass of Scotch? 02:05 - How to be Handsome!

DOUG HYPΞ ⭐️

18,867 views • 1 year ago

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways
2:02:21

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

Epoch AI

152,591 views • 1 year ago

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models
1:16:04

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models

Matt Turck

451,229 views • 9 months ago

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 views • 2 months ago

Wide-ranging and somewhat controversial conversation with Tom Bilyeu — buckle up! 00:00:00 Welcoming dark horse, Tom Bilyeu to The Inside Rail 00:01:19 Tom Bilyeu’s Philosophical Approach to Life 00:03:26 Sponsor: Fresh Pressed Olive Oil Club 00:05:22 The Conscious Approach is Having a Moment 00:12:45 Sponsor: Caraway 00:14:15 How to Make Progress Today 00:30:22 All Problems Stem from Inflation 00:34:03 Tom’s Relationship with his Wife 00:53:00 A Message for Young Women 00:55:56 How Tom Committed to His Marriage 00:59:36 Hotness and Beauty 01:06:37 Porn and a Cultural Evolution 01:17:26 Creating Ritual in Life 01:23:12 Bret’s Relationship with Heather 01:27:12 Bret’s Advice for Young People 01:29:55 The Cassandra Complex 01:43:05 Bret on Teaching through DarkHorse 01:47:33 Tom on Reaching People 01:53:50 Tom on Current Events 02:17:50 Closing
2:18:55

Wide-ranging and somewhat controversial conversation with Tom Bilyeu — buckle up! 00:00:00 Welcoming dark horse, Tom Bilyeu to The Inside Rail 00:01:19 Tom Bilyeu’s Philosophical Approach to Life 00:03:26 Sponsor: Fresh Pressed Olive Oil Club 00:05:22 The Conscious Approach is Having a Moment 00:12:45 Sponsor: Caraway 00:14:15 How to Make Progress Today 00:30:22 All Problems Stem from Inflation 00:34:03 Tom’s Relationship with his Wife 00:53:00 A Message for Young Women 00:55:56 How Tom Committed to His Marriage 00:59:36 Hotness and Beauty 01:06:37 Porn and a Cultural Evolution 01:17:26 Creating Ritual in Life 01:23:12 Bret’s Relationship with Heather 01:27:12 Bret’s Advice for Young People 01:29:55 The Cassandra Complex 01:43:05 Bret on Teaching through DarkHorse 01:47:33 Tom on Reaching People 01:53:50 Tom on Current Events 02:17:50 Closing

Bret Weinstein

50,030 views • 1 year ago

A month ago, we invited 30 of the world’s top mathematicians to Berkeley for a weekend to finish a very hard math exam. The 2025 FrontierMath Symposium wrapped up the hardest tier of FrontierMath, our benchmark for AI’s math abilities. The mathematicians tested AI models on their most challenging math problems, and discussed AI’s future in math. Watch our closing ceremony footage for a glimpse behind the scenes. -Timestamps- (00:00) - Intro by Elliot Glazer - Epoch AI (03:38) - Topology | Sergei Gukov - Caltech (06:53) - Algebraic Geometry | Ravi Vakil - Stanford (09:10) - Number Theory | Ken Ono - University of Virginia (13:19) - Combinatorics | Igor Pak - UCLA (16:54) - Analysis | Paata Ivanisvili - UC Irvine (18:18) - Closing Remarks
19:39

A month ago, we invited 30 of the world’s top mathematicians to Berkeley for a weekend to finish a very hard math exam. The 2025 FrontierMath Symposium wrapped up the hardest tier of FrontierMath, our benchmark for AI’s math abilities. The mathematicians tested AI models on their most challenging math problems, and discussed AI’s future in math. Watch our closing ceremony footage for a glimpse behind the scenes. -Timestamps- (00:00) - Intro by Elliot Glazer - Epoch AI (03:38) - Topology | Sergei Gukov - Caltech (06:53) - Algebraic Geometry | Ravi Vakil - Stanford (09:10) - Number Theory | Ken Ono - University of Virginia (13:19) - Combinatorics | Igor Pak - UCLA (16:54) - Analysis | Paata Ivanisvili - UC Irvine (18:18) - Closing Remarks

Epoch AI

21,379 views • 1 year ago

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire
58:45

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

Jacob Effron

138,223 views • 3 months ago

Here is my episode with Patrick Collison We discuss: - what it takes to process $1 trillion/year - how to build multi-decade APIs, companies, and relationships - what's next for Stripe (increasing the GDP of the internet is an open ended task, and the Collison brothers are just getting started). Plus the amazing stuff they're doing at Arc Institute, the financial infrastructure for AI agents, playing devil's advocate on progress studies, and much more. Links to other platforms below. Enjoy! Timestamps: 00:00:46 - Advice for 20-30 year olds 00:12:58 - Progress studies 00:23:07 - Arc Institute 00:35:13 - AI & Fast Grants 00:44:32 - Stripe history 00:56:30 - Stripe Climate 01:02:25 - Beauty & APIs 01:12:37 - Financial innards 01:29:02 - Stripe culture & future 01:42:42 - Virtues of big businesses 01:52:27 - John
1:57:38

Here is my episode with Patrick Collison We discuss: - what it takes to process $1 trillion/year - how to build multi-decade APIs, companies, and relationships - what's next for Stripe (increasing the GDP of the internet is an open ended task, and the Collison brothers are just getting started). Plus the amazing stuff they're doing at Arc Institute, the financial infrastructure for AI agents, playing devil's advocate on progress studies, and much more. Links to other platforms below. Enjoy! Timestamps: 00:00:46 - Advice for 20-30 year olds 00:12:58 - Progress studies 00:23:07 - Arc Institute 00:35:13 - AI & Fast Grants 00:44:32 - Stripe history 00:56:30 - Stripe Climate 01:02:25 - Beauty & APIs 01:12:37 - Financial innards 01:29:02 - Stripe culture & future 01:42:42 - Virtues of big businesses 01:52:27 - John

Dwarkesh Patel

350,541 views • 2 years ago

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future
1:03:13

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future

Raoul Pal

280,164 views • 1 year ago

Are AI benchmarks doomed? Greg Burnham and Tom Adamczewski join Anson Ho to push back on benchmark pessimism and dig into what the next generation of AI benchmarks could look like. (0:00:00) - Preview (0:00:36) - Intro: Are AI benchmarks doomed? (0:03:13) - The costs and benefits of benchmark development (0:11:48) - MirrorCode and scalable benchmarks (0:20:57) - AI speed-up in benchmark development (0:23:28) - The benchmark-reality gap (0:38:26) - Can an AGI benchmark exist? (0:43:18) - Beyond automated scoring (1:00:45) - How AI changes benchmark building in practice
1:05:24

Are AI benchmarks doomed? Greg Burnham and Tom Adamczewski join Anson Ho to push back on benchmark pessimism and dig into what the next generation of AI benchmarks could look like. (0:00:00) - Preview (0:00:36) - Intro: Are AI benchmarks doomed? (0:03:13) - The costs and benefits of benchmark development (0:11:48) - MirrorCode and scalable benchmarks (0:20:57) - AI speed-up in benchmark development (0:23:28) - The benchmark-reality gap (0:38:26) - Can an AGI benchmark exist? (0:43:18) - Beyond automated scoring (1:00:45) - How AI changes benchmark building in practice

Epoch AI

22,037 views • 2 months ago

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters
1:10:31

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 views • 6 months ago

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short
45:00

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short

Bg2 Pod

35,410 views • 7 months ago

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!
2:23:01

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!

Rob Wiblin

20,369 views • 8 months ago

Played a quick game with Daniel Negreanu (Daniel Negreanu) before the podcast. Wild how fast someone at his level can read you. We spoke about how people behave when the stakes rise, what pressure does to decision-making, how influence works at a table, and physical cues. Also got into failure, long losing stretches, early life, Vegas, reading people, chess vs poker and poker math. Very few people think this cleanly about competition and human behaviour. Timestamps below. 00:00 - Daniel's Feedback on my Game 02:56 - How to Read Body Language 05:23 - What Makes Daniel Great 11:55 - How to Influence Someone 15:17 - Dealing with Failures 18:47 - Lessons from the Game 22:44 - Poker vs Chess 26:42 - What Creates Charisma 36:02 - Thinking Under Pressure 40:52 - Daniel’s Early Life 46:36 - What Losing Teaches 50:26 - How Has Poker Changed 59:21 - High Stakes = Better Game 01:01:35 - Reading People & Poker Math 01:05:01 - Dealing with Jealousy 01:12:10 - What He Learned from His Woman 01:15:47 - Private Games in Vegas 01:22:23 - Questions for me
1:30:00

Played a quick game with Daniel Negreanu (Daniel Negreanu) before the podcast. Wild how fast someone at his level can read you. We spoke about how people behave when the stakes rise, what pressure does to decision-making, how influence works at a table, and physical cues. Also got into failure, long losing stretches, early life, Vegas, reading people, chess vs poker and poker math. Very few people think this cleanly about competition and human behaviour. Timestamps below. 00:00 - Daniel's Feedback on my Game 02:56 - How to Read Body Language 05:23 - What Makes Daniel Great 11:55 - How to Influence Someone 15:17 - Dealing with Failures 18:47 - Lessons from the Game 22:44 - Poker vs Chess 26:42 - What Creates Charisma 36:02 - Thinking Under Pressure 40:52 - Daniel’s Early Life 46:36 - What Losing Teaches 50:26 - How Has Poker Changed 59:21 - High Stakes = Better Game 01:01:35 - Reading People & Poker Math 01:05:01 - Dealing with Jealousy 01:12:10 - What He Learned from His Woman 01:15:47 - Private Games in Vegas 01:22:23 - Questions for me

Prakhar Gupta

114,188 views • 7 months ago

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?
1:42:41

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?

owl

44,279 views • 7 months ago

Human generated data has fueled incredible AI progress, but what comes next? 📈 On the latest episode of our podcast, Hannah Fry and David Silver, VP of Reinforcement Learning, talk about how we could move from the era of relying on human data to one where AI could learn for itself. Watch now → 00:00 Introduction 01:50 Era of experience 03:45 AlphaZero 10:19 Move 37 15:20 Reinforcement learning and human feedback 24:30 AlphaProof 29:50 Math Olympiads 35:00 Experience based methods 42:56 Hannah's reflections 44:00 Fan Hui joins
49:30

Human generated data has fueled incredible AI progress, but what comes next? 📈 On the latest episode of our podcast, Hannah Fry and David Silver, VP of Reinforcement Learning, talk about how we could move from the era of relying on human data to one where AI could learn for itself. Watch now → 00:00 Introduction 01:50 Era of experience 03:45 AlphaZero 10:19 Move 37 15:20 Reinforcement learning and human feedback 24:30 AlphaProof 29:50 Math Olympiads 35:00 Experience based methods 42:56 Hannah's reflections 44:00 Fan Hui joins

Google DeepMind

226,921 views • 1 year ago